(a) Sample Softmax Values:
The first step is to sample the softmax values from the Dirichlet distribution [1]}. The Class Similarity Matrix (CSM) is created which contains important information on how similar the classes are to each other. If the classes are similar, we expect the softmax values are concentrated over these labels. CSM is obtained by considering the weights of the model's last layer. Typically, any classification model has the final layer as fully-connected layer with a softmax non-linearity. If the classes are similar, we find similar weights between connections of the penultimate layer to the nodes of the classes [2]}. The Class Similarity Matrix is constructed as,

C(i,j) = wiTwj||wi||||wj||

where \(\mathbf {w}_i\)  is the vector of weights connecting the previous layer nodes to the class node \(i\) . \(\mathbf {C} \in \mathcal {R^{K\times K}}\)  is the Class Similarity Matrix for \(K\)  classes. We then sample the softmax values as,

Softmax = Dir(K,C)

where \(C\)  is concentration parameter which controls the spread of softmax values over class labels.
